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Social media has often been viewed as a retailer’s marketing channel, but with customers demanding more personalization than ever from D2C brands, social media has become a digital storefront, a customer service desk, and a community hub rolled into one platform. As platforms evolve and consumer behaviors shift, staying ahead requires more than just posting regularly or running the occasional ad campaign.

That’s why robust segmentation, audience knowledge, and personalized customer experiences are more important than ever. This post will dive into key elements of social media strategy that drive results. Whether those purchases happen in-app or on your website, a Customer Data Platform (CDP) can help you build your audience knowledge, design campaigns, and measure your efficacy.

Understand your audience inside out

You wouldn’t launch a product without understanding your target market, right? The same goes for your social media strategy. To start, leverage social listening tools within your MarTech stack to monitor conversations about your brand and industry. What are people saying? What problems are they trying to solve? Analyze your competitors’ social presence to understand where they are winning — and losing — in the market. 

Remember: different platforms attract different audiences. The age, gender, estimated purchasing power, and market segment of your target audience will often determine where on social media is best to reach them. 

Use platform-specific metrics to understand where to find your audience and how they behave. You may discover your Facebook followers crave detailed product information, but your TikTok followers love behind-the-scenes product videos.

This research, combined with accurate zero-, first-, and third-party customer data and your CDP, will help you create Customer 360s that go beyond demographics. They help gather general insights into your users’ online behavior, preferred social networks, and additional context on their interests and preferences. In aggregate, you can quickly understand whether short video content on TikTok or a strong visual brand aesthetic for Instagram will attract your customers and plan work accordingly. 

Once this information is in place, you can create segments accordingly and personalize messaging to each segment. Rather than guess which segments will be interested in your products demographically, you can leverage psychographic information to demonstrate precisely how well you know your customers and serve them only what they are interested in. 

Conversely, you may already know where your customers spend their time online but are curious about how to message across those channels to create an omnichannel experience. A CDP can help you tailor your messaging for each platform, and the specific audience you know interacts with your brand on that platform.

Create compelling content for social media marketing

Whether you have a strong following on one platform or customers are split across platforms, you can use a CDP to help determine how to prioritize your channels and when and whether to repurpose content and tailor your approach to each platform.

If you market to Gen Z audiences and have aligned your brand strongly with modern aesthetics, it makes sense to grow an audience on TikTok, where trends most frequently arise, and you can capitalize on them quickly. 

Once you’ve created videos, you can also identify secondary markets that fit your persona’s behavior: an effective strategy many retailers have chosen is repurposing TikTok videos to Instagram Reels to reach younger millennials. 

This works primarily because of commonalities among users of both TikTok and Instagram — using an example of a fashion retailer, similar content should perform well on both TikTok and Instagram because both platforms prioritize fashion content, and the purchasing habits of both groups indicate that social media plays a large part in influencing purchasing decisions. 

Another strategy some take is offering special discounts to your social media followers in exchange for a deeper connection to them, such as providing 10% off first orders when you sign up for an email newsletter or survey.

Harness the power of influencer partnerships

There’s plenty of buzz around influencers — and for good reason. Influencer marketing on social media is a powerful tool when used right. First, look for influencers who align with your brand values and have an engaged audience that matches your target demographic.

Influencer marketing is where you can get creative. Consider product seeding, co-created content, affiliate programs, and account takeovers. For example, you can partner with a skincare influencer to create a 30-day skin transformation challenge using your products.

Here’s where you can get creative. Consider product seeding, co-created content, affiliate programs, and account takeovers. For example, you can partner with a skincare influencer to create a 30-day skin transformation challenge using your products.

If you’re using influencer marketing, measure reach, engagement rates, sentiments in comments, and, most importantly, conversions.

Optimize paid social advertising

Organic channels are essential to any marketing strategy, but to move the needle, consider paid advertising strategies. Every social media platform offers ads as another way of reaching people who fit your target audience but may not necessarily follow you or be aware of your business. 

Some successful paid strategies include retargeting users who have shown interest in your products but haven’t converted or recently abandoned their carts or using interest-based targeting to reach users who follow competitors or relevant influencers.

Once you know where your ideal audience lives, you can choose ad formats that showcase your products best on each channel. Video ads can demonstrate product use and results, whereas Instagram Story ads can offer immersive experiences.

Next, get smart with targeting. Using your MarTech stack and CDP, you can identify anonymous and unknown users on your sites, create lookalike audiences based on your best customers, and segment accordingly. Some successful strategies include retargeting users who have shown interest in your products but haven’t converted or recently abandoned their carts or using interest-based targeting to reach users who follow competitors or relevant influencers.

The key is to continuously test your messaging and channels. Compare different ad copy, visuals, CTAs, and landing pages, then let the data guide your decisions. With the right strategies in place, you can track ROAS, take advantage of the boost in visibility from a successful initial campaign, and iterate.

Build a thriving community on social media

Social media goes beyond broadcasting your product. Nowadays, consumers want to be engaged with and entertained by tailored content. Set up systems to ensure timely responses to comments and messages. Some brands rely on chatbots for instant responses to common queries. 

Encourage and showcase user-generated content, such as creating a branded hashtag or featured customer stories. This makes your customers feel valued and provides social proof for your brand. Finally, foster engagement with interactive content. Polls, Q&As, and challenges can get your audience engaged, and the more they interact with your brand, the more connected they will feel.

Refine your audience personas

As you build rapport with your target audience using your initial content strategy, your CDP will gather additional data points on their interests. There is a difference between people interested in fast fashion and those who are interested in sustainable fashion, also known as “slow fashion.”

You can use this information to create better-targeted ads. To generalize, someone who shops fast fashion may not be swayed by an ethically sourced and handmade quilt coat like someone interested in slow fashion might be.

If you’re using a Cloud Data Warehouse and CDP together, your customer data will help showcase customer preferences. They can also offer insights into which factors in a user’s life may determine their purchasing habits, buying power, and when they tend to purchase more frequently. 

You can use this information to craft personalized content that helps your audience better understand their alignment with your brand, such as emphasizing the cyclical nature of your clothing business to a slow fashion audience or your commitment to stylish clothing made by workers who are paid a living wage. 

You can also offer discounts timed for the moments in an audience’s purchasing cycle to incentivize purchases before specific events like weddings, school seasons, or seasonal changes. 

Activate and respond to insights in real time with a CDP

CDPs can revolutionize your real-time audience insights. Simon’s identity resolution feature, for example, can help you pair known customer profiles with your website traffic. It allows you to target better and message customers who abandon their carts or have visited a specific product page multiple times. 

With this information, you can set rules to trigger pop-ups or emailed discounts when someone has visited a product page more than three times in a month. Simon’s out-of-the-box predictive models can also help you determine who to send such an offer and when to deploy those offers. 

You can also nudge a prospective customer into purchasing something they have considered on your site by running ads that feature that specific product. For example, an ad could show someone how to style a garment they’ve been looking into, by either offering styled outfits or videos from influencers you work with, showing how they’ve incorporated the garment into their wardrobe.

A CDP can also give you insights into when campaigns derail. If a campaign reaches an unintended audience, you can rein it back in to protect your engagement data and more accurately measure reach. It can also offer you information about why the campaign derailed — perhaps an influencer in multiple markets cross-posted something, or a common hashtag was repurposed and gained new meaning. 

Even if the answer isn’t as obvious, figuring out what does work will be easier with a CDP, as you can access other insights into your customer’s interests and desires to design toward.

Measure and improve social media marketing strategies

Your social media marketing strategy shouldn’t be set in stone. Use your CDP and data to continuously refine your approach. Track key metrics like engagement rate, conversion rate, and customer acquisition cost (CAC). And don’t just collect data — analyze it. Look for trends and insights that can further inform your strategy.

The best part about using a CDP is its all-in-one nature: once you build a campaign, you can track your social media ROI using CDP analytics. For both short-term and recurring campaigns, you’ll build a knowledge base about your customers’ behaviors that is accessible in the CDP and can therefore feed back into your segmentation model.

For example, a cohort of customers that regularly interacts with your social media can be suppressed from ads in their feed. Similarly, you can identify customers with a high LTV and remarket to them easily — and specifically — by designing new programs or offerings that are tailored to their buying habits. 

All of this can help reduce your customer acquisition cost and encourage repeat purchases — while your existing campaigns are still running.

Conclusion

Creating a successful social media strategy for your brand isn’t about jumping on every trend or being on every platform — and figuring out where and when your customers are most likely to make purchases doesn’t need to be complicated.

Using a CDP like Simon Data’s can streamline insights into your social media presence by providing a single source of truth about how your customers are interacting with your content, which allows you greater flexibility when designing campaigns and can strengthen your segmentation. This means you can personalize marketing experiences, offer only the most compelling deals at critical times, and iteratively refine your strategy as you learn more about your audience.

Believe it or not, the holiday shopping season is right around the corner, and marketers are under pressure to deliver winning campaigns that drive sales and engagement. Black Friday and Cyber Monday (BFCM) present a unique opportunity to kick off the season on a high note—but only if you have the right data and insights to fuel your strategies.

At Simon, we can help. Our CDP empowers marketing teams to transform their BFCM approach and drive more revenue by unifying offline and online customer data, gather real-time insights, and activating segments that create the personalized experience customers crave.

From leveraging Black Friday purchase history to building smarter audience segments, you'll discover proven tactics to maximize your holiday sales and engagement. This post delves into how Simon Data empowers marketing teams to harness Black Friday's in-store point-of-sale (POS) data and forge it into Cyber Monday's success.

Still marketing BFCM the old way? It's holding you back.

If you're still wrestling with siloed data and sluggish insights, you're fighting with one hand tied behind your back. Siloed information across online and offline channels cripples personalization efforts.

During BFCM, delayed in-store and point-of-sale data limits your marketing responsiveness to critical customer engagement, such as real-time purchases, abandoned carts, and customer trends — a recipe for missed business opportunities and wasted resources.

Unlocking in-store data for actionable insights with Simon Data

We all know the struggle of juggling in-store and online customer data. That’s why we built Simon — it’s a powerful CDP that seamlessly blends your in-store POS information with online customer behavior. 

The result? A clear, comprehensive view of your customer's journey that you can actually use. Here are the biggest benefits of using the Simon Data CDP during BFCM.

Real-time data integration

Simon stands out by seamlessly integrating in-store POS data from tools like Shopify’s POS System, with online and other customer data sources, empowering marketers to take immediate action based on the most current customer interactions, both online and in-store.

Unified customer profiles

By creating a single, unified view of each customer, Simon Data CDP enables hyper-personalization at scale. 

This comprehensive profile (what we call a customer 360) includes online browsing history, purchase records, and in-store interactions, providing a complete picture of customer behavior and preferences.

Advanced segmentation 

Simon Data CDP's advanced segmentation capabilities allow marketers to create laser-focused audience segments based on real-time in-store behavior and online interactions. This granular approach ensures that each customer receives the most relevant and timely communications on their preferred channels during the critical Cyber Monday push.

How to use Black Friday in-store customer data for Cyber Monday personalization

The Black Friday rush can leave marketing teams scrambling to keep up, let alone deliver personalized experiences. But what if you could transform that in-store frenzy into a strategic advantage for Cyber Monday? 

With access to accurate in-store data, marketing teams can create highly targeted campaigns that drive engagement and boost sales. Here are some of the ways our customers use the Simon Data CDP during the holiday season.

Creating personalized campaigns that drive engagement

Using Simon’s CDP, customers can easily analyze Black Friday in-store activity to identify customers who've already purchased or wishlisted specific products. 

Then, they can craft exclusive Cyber Monday offers that complement these purchases and send out email and SMS campaigns through MarTech tools like Attentive, creating a seamless shopping experience that makes customers feel like VIPs.

Building smarter lists to maximize ROAS

BFCM calls for plenty of spending on paid media. But spending on paid channels without seeing a return is a marketing team’s nightmare. Simon’s CDP helps marketers create precise exclusion lists, ensuring they don’t waste budget on irrelevant ads and Cyber Monday campaigns reach the right audiences with the right messages.

Staying ahead of BFCM with real-time campaign optimization

With Simon, marketers can monitor and adjust Cyber Monday campaigns on the fly. A CDP ensures marketing teams are not just reacting to trends — they’re setting them while ensuring their efforts remain relevant throughout the day.

Tips for advanced BFCM segmentation with Simon Data

Simon Data's advanced segmentation capabilities go beyond basic demographics and purchase history — and even Black Friday. By analyzing customer interactions across channels, our customers create:

Location-based personalization that bridges online and offline customer experiences 

With Simon, marketers can target customers within specific radiuses of their physical stores. Brands can promote local inventory, highlight nearby deals, and create a cohesive omnichannel experience that drives both online conversions and in-store traffic.

Make seamless product recommendations

During BFCM, identify customers who browse online but prefer to purchase in-store. Then, deliver email campaigns that seamlessly blend their digital and physical shopping experiences.

Simon allows brands to include additional contact properties or contacts events in content for email campaigns that create a cohesive in-store and online experience for their customers. Here’s an example:

Let’s say a customer purchases a cooking appliance in person in-store. Simon Data then receives an event showing the different SKUs that the customer purchased in-store (i.e., an espresso machine). 

Simon then triggers an email that references the espresso machine and surfaces adjacent product recommendations (i.e. espresso kit accessories, such as a tamper and portafilter) for the customer to buy.

Suppress recent purchasers to optimize paid media spend

BFCM can easily lead to wasteful ad spending if it’s not managed carefully. Simon Data’s CDP gives marketers the power to avoid this pitfall by identifying and suppressing recent purchasers from paid media campaigns.

Let's say you notice a spike in in-store sales for a particular product line during Black Friday. Simon's real-time data integration allows you to quickly create an exclusion list of these recent in-store buyers.

You can then apply this list to your Cyber Monday paid search, programmatic acquisition marketing (think The Trade Desk or Criteo), and social media channels like TickTock, Snapchat, and Instagram — ensuring your budget is focused on prospective customers rather than those who have already converted.

This targeted approach not only saves money but also prevents customers from being bombarded with redundant offers. It's a win-win that preserves your marketing ROI and enhances the overall customer experience.

Capitalize on price drops with tailored campaigns

BFCM is a prime time for retailers to offer deep discounts and limited-time deals. However, simply broadcasting these price drops isn't enough — you need to ensure they're reaching the right customers at the right time.

While these strategies are extremely useful for BFCM sales, they’re also valuable year-round, so be sure to implement them throughout your marketing campaigns beyond the holiday season.

Simon Data CDP's advanced segmentation capabilities make this possible. By analyzing browsing data, Simon can identify customers who have recently viewed products that are now subject to price reductions. 

You can then trigger personalized email campaigns or retargeting ads highlighting these discounted items, creating a sense of urgency and excitement around the savings.

This level of contextual relevance is crucial during BFCM when customers are inundated with promotional messages. By delivering price drop alerts to shoppers who have already demonstrated interest, you increase the likelihood of driving conversions and maximizing the impact of your limited-time offers.

In addition, Simon Data CDP's advanced capabilities allow you to:

1. Group customers based on cross-channel interactions
2. Predict high-value Cyber Monday shoppers (and the items they’re looking to buy!)
3. Tailor campaigns to customer lifecycle stages
4. Focus premium offers on your most profitable segments
5. Identify cross-sell and upsell opportunities based on a comprehensive purchase history

While these strategies are extremely useful for BFCM sales, they’re also valuable year-round, so be sure to implement them throughout your marketing campaigns beyond the holiday season.

The bottom line: Transforming your BFCM strategy

Don’t let the stress of Black Friday and the frantic rush of Cyber Monday hinder your marketing strategies. Combining a cloud data platform like Snowflake with a CDP like Simon Data is the secret weapon for turning those chaotic shopping days into a golden opportunity. 

Thanks to Simon’s powerful capabilities, marketing teams can gain real-time insights from their online and offline worlds to create the ultimate shopping experience for customers during BFCM and beyond.

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For over ten years, Simon AI has always been at the forefront of leveraging data and technology to empower marketers. With the rapid advancements in GenAI, our team wanted to take a thoughtful approach to understand its implications and how to use it appropriately within our platform to provide real value to our users. 

In this post, Simon’s content marketing team sat down with Simon AI product designer Hana Kurihara, who shared insights from our recent AI research and discussed Simon’s approach to integrating GenAI into our product strategy. 

Hana, can you tell us about Simon AI's recent exploration into GenAI?

Of course! We recently conducted interviews and surveys with our users to understand their needs better. What's interesting is that we intentionally avoided mentioning GenAI at first. We wanted an unbiased view of what marketers are struggling with and how they're using our platform.

‍That's an intriguing approach. What did you discover?

We found that the biggest challenge for our users is making the most out of their customer data when creating campaigns. It's fascinating because this aligns perfectly with Simon AI's core mission as a CDP and GenAI's strengths.

So how does this finding influence your approach to incorporating GenAI into Simon AI's platform?

It's really shaping our strategy. We're focusing on how GenAI can help marketers better understand and utilize their first-party data. We're first looking at areas like improving data cleanliness and aggregation, which are crucial for getting the most out of GenAI and any data-driven marketing efforts.

Can you give us an example of how GenAI might help?

Sure! Let's consider data cleanliness, for example. GenAI could help identify and standardize inconsistent data entries, like variations in address formats or product names. Aggregation could assist in creating more sophisticated customer lifetime value (LTV) models by identifying patterns in purchase history that humans might miss.

That sounds promising. What AI features are already available on Simon’s platform?

We’ve started integrating GenAI in a few key areas. We currently have a Jinja generator in our template editor. It’s a nifty little tool that helps you assemble Jinja code blocks for email templates to personalize emails. We also have a segment description generator that automatically generates a description for your segment as you build it. Essentially, it gives users a quick, easy way to know who is in the segment. These features are just the beginning, though. We're excited about the potential to do much more!

How do you see AI changing the game for B2C retail marketers specifically?

For retail marketers, I think GenAI will be a game-changer in personalization and campaign efficiency, such as having the ability to quickly generate highly tailored product recommendations or create dynamic content that adapts to individual customer preferences at scale. 

A huge plus is the ability to automate and streamline tasks, but we’re looking to go beyond that by using AI to uncover insights and opportunities that might be hidden in your customer data.

Are there any challenges you foresee in implementing AI?

Definitely. One of the biggest challenges is ensuring that the AI models can work effectively with each of our customer's unique data sets. Every retailer has its own product catalog, customer segments, and historical data. We need to make sure our GenAI solutions can adapt to these differences while maintaining accuracy and relevance.

Looking ahead, what excites you most about the future of AI in marketing?

What excites me most is the potential for GenAI to democratize data science for marketers. Not every team has a data scientist, but using GenAI, more marketers can leverage advanced analytics and insights. 

I'm also thrilled about our partnership with Snowflake and how it could allow us to process first-party data more efficiently, leading to faster, more personalized insights for our users.

Any final thoughts?

I'd encourage marketers to start thinking about how they can prepare their data for the GenAI revolution. Clean, well-structured data will be crucial for getting the most out of these tools. And stay curious! The field is evolving rapidly, and there will be many exciting developments to come.

Remarketing and retargeting aren’t quite the same thing. Both are marketing strategies that have some overlap, and sometimes their terms are used interchangeably, but the distinguishable differences mark them as two different techniques to reach customers.

Let’s outline these differences so you’re able to retarget and remarket with confidence.

What is remarketing?

Remarketing involves re-engaging customers who’ve already purchased or subscribed to you in order to entice them to return — hence, remarketing. Remarketing campaigns aim their sights and customers who’ve churned or who haven’t recently interacted with your brand.

You may notice this term being used interchangeably with retargeting. For instance, Google Remarketing Services offer retargeting. For the sake of clarity, we can use them distinctly.

How remarketing works

Collecting user data

To start remarketing effectively, consider a place to centralize, normalize, and activate your data. A good Cloud Data Warehouse paired with an even better CDP should give you all the tools to collect user data and use it effectively.

The data collection process is essential. Do you use zero- or first-party data to track user behavior? How about third-party cookies? Do you track campaigns with UTM tags to see who converts? These are all valuable data sources you can centralize in one place to identify users that are ideal for remarketing.

Segmenting audiences

Audience segmentation is key to effective remarketing. Your goal is direct response to your campaign, be that an email signup, a form fill, or a product purchase. You target the right users for each action by segmenting an audience by behavior.

For instance, dormant customers will benefit for an email to reengage them, and you can segment for dormant customers by looking for customers who completed a purchase months ago and haven’t returned. Or, you could interact with a churned subscriber through a paid ad asking them to come back. To do this, you create an audience segment for users who recently unsubscribed.

Creating the right ads

Effective remarketing turns one-time buyers and brief subscribers into loyal customers. This involves multiple touchpoints across several channels to engage customers.

This is where you need to know your customer journey. Where do customers hang out? How do the ads look there? And possibly most importantly, what’s your budget? Answering these questions can help you create standout ads.

Measuring success

Effective remarketing carefully measures each channel’s success. You can tweak campaigns live depending on whether users have taken a desired action. 

Remarketing relies on your customer data to gauge effectiveness. Are customers coming back? Which touchpoints are causing a renewal? To create these insights, you have to store customer data correctly.

Who uses remarketing?

Remarketing is an effective strategy if you’re looking to increase customer lifetime value (CLV) and drive repeat purchases. You’ll see these crowds using remarketing often:

• Ecommerce: Ecommerce uses remarketing to promote related products, offer post-purchase incentives, or get buyers back to their page with new sales and discounts.
• Subscription services: Businesses with subscription models use remarketing to upsell additional services to existing subscribers or win their churned subscribers back.
• Event-based businesses: Companies that sell tickets or register attendees for events can use remarketing to follow up with past attendees and encourage them to sign up for future events.

Remarketing examples

There’s no one way to do remarketing. Almost any channel you use for regular marketing can be for remarketing, too. Here are some thoughtful ways to approach remarketing.

Win back strategy

For customers who haven’t interacted with your business in a while, a remarketing email campaign might offer a special promotion or new product announcement to bring them back.

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In the case above, remarketing is particularly useful since the customer needs service regularly. So, Jiffy Lube schedules an email to send once a customer has lapsed an oil change for a certain number of months.

Paid ads

Have you ever seen an ad on social media enticing you to resubscribe? Paid media is a good place to keep your brand top-of-mind with users, particularly if your content includes enticing creative that meshes well with the feed:

SMS

Once a customer has given you their phone number, you can message them about exclusive deals. Just don’t abuse this power and text every day.

SMS can be a very effective channel. Most people will drop everything to check a text notification, so with the right incentive, customers might subscribe to texts to see your offers.

Reengagement emails

After a customer makes a purchase, a remarketing email might suggest complementary products or services. After all, the customer liked your brand enough to try it once!

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What is retargeting?

Like remarketing, retargeting is a form of online advertising that invites users to return to your product or service. However, retargeting focuses on users who have previously visited your website or interacted with your content but did not complete a desired action, such as making a purchase or filling out a contact form. Conversely, remarketing is for people who are already users of your product.

The primary goal of retargeting is to reengage potential customers and guide them further down the sales funnel.

How retargeting works

Collect user data

Like remarketing, retargeting relies on tracking technology, typically in the form of cookies or pixels, to monitor user behavior on your website. When a user visits your site, a small piece of code (a pixel) is placed on their browser. This pixel tracks the user on your site or app and records their actions. This is essential to find the users who enter their contact info on a sales or subscription page but don’t follow through!

Create retargeting event triggers

Event triggers are essential to effective remarketing; it’s important to strike while the iron is hot. You do this by working with a tool or integration that uses real-time data to identify users who don’t complete an action.

From there, you can tailor particular event triggers that will send the user social media ads, text messages, emails, and so on (though hopefully not all at once!).

Entice users back

Now, with the right triggers in place, your ads can work to entice users back and finish what they’d started. Enticing creative, contextual advertising, and the right incentives help bring users back.

Additionally, don’t underestimate the power of multichannel marketing in winning users back. Try running campaigns on more than one channel.

Optimize for success

Successful retargeting involves several potential points of failure. You’ll need to monitor CRO on the landing page, ROAS on paid ads, and set frequency caps to prevent frustrated users fatigued with ads. A good retargeting campaign requires carefully monitoring data and feedback.

Who uses retargeting?

Retargeting can be used by nearly any industry. Here’s where it’s most common:

• Ecommerce stores: As with remarketing, ecommerce stores benefit greatly from strong retargeting campaigns. Paid media and search ads cater to products with clear imagery.
• B2B marketers: For businesses selling high-value services or products, retargeting nurtures leads that slip through the cracks or aren’t ready to commit to a big price tag.
• Service providers: While you may have to get creative with the promos and messaging you use, service products can use retargeting to stay top-of-mind for potential customers researching their options.

Retargeting examples

Retargeting is tricky; the message is crucial in encouraging a user to finish a purchase. Here are some ways companies can use a multichannel approach to market to users who bounce.

Product-specific SMS

If you notice a customer eyeing a specific product, retargeting for that specific product can be powerful. You could choose to display the product dynamically on paid media, or you could use SMS to send a push notification to allow the customer to pick back up where they left off:

Cart abandonment emails

Email is one of the easiest bits of contact information to obtain. Because of that, many companies opt for email retargeting to get users back:

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Display ads

If your user is seeing a product everywhere, chances are they’ll finally reunite with it when they’re ready to buy. Display ads served on the websites they’ll visit give them a small push to reconsider a purchase:

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Retargeting vs. remarketing: When to use each

Bottom line, retargeting and remarketing and similar techniques to reach different audience segments. Here’s a quick guide on when to use each strategy:

The right decisions are with the data

Both retargeting and remarketing are what separate passive marketers from agile ones. By understanding the differences between these two strategies and knowing when to apply each, you can create more targeted campaigns.

These techniques require real-time data, audience segmenting, and advanced triggers to work out flawlessly. Consider using a CDP like Simon Data to activate your customer data and get the sales rolling in.

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Data composability represents a paradigm shift in data management by emphasizing integrating data-centric applications directly on a cloud data warehouse architecture. This approach contrasts traditional data strategies prioritizing data centralization over application-driven value.

By focusing on the interoperability of data components and their seamless interaction within a composable framework, organizations can achieve greater agility, efficiency, and scalability in their data operations. 

You can read my previous article about the importance of a data composability strategy and the various applications essential for leveraging your centralized data. 

But here, I want to delve deeper into data composability so you can understand how a composable CDP enhances these applications and revolutionizes how your organization harnesses data for strategic advantage.

Data composability creates a flexible, scalable, and interconnected data environment where components — like data sources, models, and applications — can be assembled and reassembled to meet evolving business needs. 

This approach ensures that your data assets remain dynamic and adaptable, enabling rapid innovation and responsiveness. With data composability, you can be confident that your data strategy is future-proof and ready for whatever changes come your way. 

What is data composability, exactly?

Data composability is the data-centric counterpart to application composability, akin to how composable commerce leverages the MACH architecture (microservices, API-first, Cloud-native SaaS, headless). It emphasizes the interactions of data applications built directly on top of a cloud data warehouse architecture.

• Apps are data applications on top of the cloud data warehouse architecture
• Where possible, data remains within the CD, and most apps “bring compute” to the data
• It is composed of a logical pipeline of modular stages with bounded context and robust interfaces
• Extends end-to-end beyond modeling, through data applications, and back to generated data capture

It also fundamentally exposes data to an organization throughout its traditionally CDP-managed lifecycle. Rather than internally processing data opaquely and then exporting it into downstream marketing channels, a data-composable approach allows an organization to power additional, potentially non-CDP, use cases such as inventory management, supply chain optimization, or customer service improvement from each stage of data processing and keep the data within the CDW throughout more of the process.

Data composability principles

Let’s break down the core principles of data composability.

Data proximity: data predominantly remains within the CDW, minimizing data movement. Most applications bring computing to the data rather than transferring data to the computing environment, enhancing efficiency and security.

Modular design: The data processing pipeline is structured as a series of logical, modular stages, each defined by a bounded context with a robust interface. This modularization ensures a clear separation of concerns and facilitates easier management and scalability.

End-to-end integration: The composability extends from data modeling through various data applications to the capture of the data they generate. This comprehensive approach ensures a continuous loop of data utilization and enhancement.

Cross-organizational accessibility: Data is made accessible at all stages of processing and modeling, enabling a wide range of cross-organizational use cases. This openness allows for greater collaboration and innovation across different departments.

Extensible utilization: By exposing data at various stages, data composability supports extensible uses, from advanced analytics and machine learning to real-time decision-making and operational reporting.

How is that different than a traditional SaaS CDP?

In traditional SaaS CDP environments, data is often exported to and processed in a separate system, limiting visibility and control over the data. 

These platforms typically use a closed environment for processing, and scalability depends on the provider’s capabilities, which may not always align with your organization’s growth or needs.

Conversely, a data-composable CDP utilizes the cloud data warehouse architecture to keep data centralized and secure within your cloud environment. This approach reduces costs associated with data movement and enhances security by leveraging cloud-native protections. 

The flexibility of integration and customization allows businesses to tailor the system to their specific requirements, promoting a more effective and efficient use of data.

Furthermore, the data composable model provides unparalleled transparency in data operations, giving organizations complete oversight of how data is handled, transformed, and utilized. 

This open access to data at all stages, coupled with the ability to scale effortlessly using cloud resources, sets the data-composable CDP apart from traditional models, making it a superior choice for businesses looking for agility and deep data integration into their strategic operations.

How is that different than reverse ETL?

Reverse ETL vendors can provide you with a head start in tooling that lets you put together a stage more quickly; however, you’ll find their view is limited to how components work with maybe some illustrative recipes for integrating components into a use case but lacks the broader strategic view of your whole ecosystem.  

They place the burden of composability on the user, requiring significant technical skills such as SQL expertise and integration know-how. This can make it acceptable for simpler data tasks but increasingly complex and labor-intensive for more sophisticated data scenarios.

In contrast, a composable CDP is inherently designed to ensure data composability across various stages of data handling without requiring users to possess excellent technical skills. 

Thanks to its largely no-code interfaces and AI-assisted functionalities, a composable CDP democratizes data access, allowing users from various backgrounds to engage effectively with data. 

It simplifies operations and specifically supports critical business functions like marketing, advertising, and customer support, making it highly relevant and tailored to these domains.

Moreover, while reverse ETL can sometimes struggle with more complex data requirements, composable CDPs are explicitly optimized to handle intricate data scenarios. 

This ensures that even the most challenging data workflows are streamlined and efficient, minimizing the user’s effort and maximizing the business value extracted from the data.

Data composability benefits

Data composability offers several benefits:

Cross-org utility

Each module in the pipeline is designed with a bounded context, ensuring that interactions between modules are well-defined and manageable. This design allows different departments within your organization to effectively tap into the data pipeline. 

For instance, your data science team can access marketing user profiles or behavioral datasets to enhance their models, applying insights gained within marketing strategies and across various operational realms of the organization.

Security and governance

With data composability, your organization maintains complete control over the security and governance of your data. This control defines who can access the data and what actions they can perform with it. 

As the data remains within your CDW, you leverage your cloud environment's inherent security protocols and compliance measures, enhancing the overall protection of your data assets.

Swappable components

The architecture of a composable CDP is inherently modular. Each stage of the data pipeline acts as a distinct module, allowing you to customize your setup by selecting the best components suited to your needs — whether those are provided by your CDP vendor, sourced from leading technologies in the ecosystem, or developed in-house. This modularity fosters innovation and ensures your data architecture can evolve without disruptive overhauls.

Cost efficiency

One of the primary advantages of data composability is the significant cost savings. Keeping data within your cloud data warehouse and minimizing replication saves on storage costs and reduces the operational complexities of managing multiple data copies. This efficiency is financial and temporal, as it streamlines data management processes, saving time and resources.

Theory vs. reality

This diagram lays out many concepts in the straightforward logical pipeline structure I’ve been using to describe data composability. The boxes represent stages of data processing and data applications, and the arrows represent the data interfaces between each.

In reality, there are many other domain concepts and more sophisticated interactions between the stages of data processing.

One strong advantage of selecting a composable CDP to begin tackling powering these data applications for your organization is that you can draw upon the CDP’s applications as well as the data-side artifacts that drive them. 

Of course, Simon has expertise derived from years in the market and supporting use cases across a wide range of business models at all stages of data maturity. Simon partners with our clients in a way that goes beyond simply integrating the CDP and instead focuses on your broader composable data strategy.

However, one primary reason to look to a composable CDP to handle your data applications is that your upstream business model likely has a very similar structure. 

Though you can likely describe the overall business model succinctly, growing and scaling the business leads to complexities as your product and service offerings increase and your customer base expands in volume, geography, and more.

Hand-selecting the components that best fit your organization from those offered by a composable CDP lets you direct your time, effort, and focus on the most unique and most valuable aspects of your business, all the while ensuring that you have proper structure downstream to enable your marketing, advertising, and support teams.  

Selecting a composable CDP means that as you need new capabilities, you have a starting point with its suite of components, so you are never starting from scratch.

Data beyond customer data

In much the same way that a data composable approach keeps data in the cloud data warehouse for easy access by the rest of your organization, the data of the rest of your organization is also more easily accessible by a composable CDP because there’s no need to establish a heavyweight ETL process to get it there.  

Gone are the notions of “SQL traits” or “linked audiences” that take a day to backfill and only update every day after that. And there’s no “data refresh” for either the customer or the non-customer data because it becomes available soon after it lands in the CDW.

In a data-composable approach, including non-customer data is a matter of simply joining tables. This can be done throughout the logical data composable pipeline in modeling, segmentation, or personalization.

Care must be taken not to introduce undue or unsupported dependencies between composable pipeline stages nor to violate bounded context or interfaces.  

Simon Data provides high-level guidance for data modeling that is not demanding or inflexible. This ensures that it is compatible with any business model and structured just enough to maintain downstream composability.

Data modeling in the Simon CDP

 Simon divides the world into four categories of inbound data.

Identity data can arise from anywhere in your organization and only need to provide associations between contact identifiers.  It is treated specially, as it is put through rigorous entity resolution to cleanse and deduplicate it due to the sensitivity of identity resolution to bad data.  

Additionally, identity resolution is a unique modeling process that not only ensures a model tailored to marketing, advertising, and support use cases but also goes through a rigorous QA process and a versioned deployment framework before being utilized in the downstream pipeline.

Contact data is subdivided into data that maps 1-1 to a contact and many-1 to a contact, primarily designed for modeling contact properties and events.  Profiles within Simon are “logical,” only assembled and materialized on an as-needed basis. 

However, several data applications (like trait syncing) will materialize the profile and then monitor it for changes.  The cornerstone of the contact profile is identity, which is why it receives elevated treatment.

Lastly, non-contact data can be joined to the contact profile on any property, not just contact identifiers like contact data. This permits “enrichment” of the profile with external data like product listing details, fulfillment data, and more, and is generally very use case- or campaign-specific in its application.

The structure and information in this data model are provided to Simon through our identity resolution and schema explorer applications. Essentially, the user offers Simon with enough metadata to power downstream data applications appropriately, and those applications are where Simon truly begins to unlock the value of your Snowflake data. 

Conclusion

In this deep dive into data composability, we've explored how this approach can revolutionize your organization's use of data. By centering on data applications and maintaining data within the cloud data warehouse, data composability offers unparalleled flexibility, scalability, and efficiency.

A composable CDP, like Simon Data, is a cornerstone of this strategy. Providing a framework for data modeling, integration, and application development empowers organizations to unlock the full potential of their data so that businesses can be well-positioned to thrive in the data-driven economy.

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The secret to improving customer experience is in understanding that it is not a single event. It is, in actuality, a collection of interactions over the lifetime of the customer’s engagement with your brand, each with a different objective for the customer and (hopefully) a unique brand experience responding to it.  

How, then, do you ensure that you are making the most of each and every customer moment?  How do you guarantee that you are both answering your customer’s needs and delighting them in the process?

A strong solution is customer journey mapping.

Customer journey mapping involves mapping out the entire experience a customer goes through when engaging with your brand, from the first point of contact to post-purchase interactions to ongoing engagement.  

This helps companies visualize and understand their business from the customer's perspective, identify pain points, and find opportunities for improvement.

The benefits of mapping the customer journey

We’ll walk through how to develop a customer journey map in a moment, but let’s start with some specifics on why the exercise is worth the time. There are several ways it will help you optimize your marketing efforts. Here are six of the biggest:

1. Identify customer pain points and gaps

Customer journey mapping helps in identifying pain points and gaps in the customer experience. In fact, they will likely become apparent as soon as you start walking through the experience and cataloging steps and touchpoints. 

Don’t worry, every brand has these pain points. By understanding where customers face difficulties, you can develop targeted solutions to address these issues, leading to improved customer engagement, revenue and loyalty.

What’s more, if you extend the exercise cross-functionally, the journey map provides a structured approach for idea generation. It allows teams to brainstorm specific improvements at each touchpoint, leading to more focused and actionable ideas. 

2. Create a more consistent customer experience

As brands and companies grow, it is amazing how often messaging becomes disjointed across channels and customer life stages. Beyond simple messiness, this inconsistency weakens customers’ perception of the brand, eroding long-term engagement and loyalty. 

Customer journey mapping helps ensure that all channels within a life stage convey the same message and tone, reducing the risk of confusion and strengthening brand engagement. 

Once addressed, you can leverage your Customer Data Platform (CDP) to operationalize that consistency by centralizing and standardizing communication strategies for each customer segment and life stage.

3. Expand your multi-channel marketing strategy

Beyond driving consistency of messaging, journey mapping helps identify cross-channel opportunities and ensures that all channels and platforms are being brought to bear throughout the customer experience.

It highlights untapped channels that can be leveraged to reach and engage customers more effectively. This is true not just of digital channels such as paid media, email and SMS, but also channels such as direct mail and customer care.

In fact, if your brand includes physical products (rather than digital-only), one important touchpoint to remember is the physical delivery — the package that arrives at your customer’s home. 

You’ll have 100% open rate on that package, so thinking through the inserts, surprise-and-delight bonuses, and on-package messaging can be a big unlock for the customer experience.

4. Optimize all aspects of customer lifecycle

A key benefit of journey mapping is that it provides a holistic view of the customer lifecycle, from the user’s very first visit through long-term usage and loyalty. Consistency of voice and experience is important here also.  

While it’s crucial that customer communications evolve over time (as the customer learns more about the brand and the brand learns more about the customer), the user should never feel like they are suddenly engaging with a different company. A full journey map will highlight those inconsistencies with eye-opening clarity.

Similarly, by mapping out the entire customer journey, businesses can ensure that all aspects of the customer lifecycle receive focus for improvement. This includes often overlooked stages like gift-giving, and advocacy/referral. Investing a little time and attention in these stages can enhance revenue, base growth and loyalty. 

5. Increase “always on” marketing

An effective customer journey map will identify key moments for triggered and automated journeys, often referred to as "always on" marketing. These automated journeys can provide timely and personalized interactions that keep customers engaged and moving forward in their journey.  

For example, “win back” efforts can be triggered when someone hasn’t visited or purchased in three months, and can consist of a series of automated communications designed to re-engage the customer and get them purchasing again. A CDP enables automated journeys based on real-time customer data and behavior, creating greater efficiency than weekly campaign-based blasts.

6. Amplify personalization

Thinking like the customer allows businesses to spotlight key moments for personalization. This can include customized messaging, relevant content, and product recommendations based on the customer's behavior and preferences. 

Personalization enhances the customer experience and increases the likelihood of near-term conversion and long-term loyalty. In providing a detailed view of all the touchpoints in the customer experience, journey mapping is the perfect exercise to find and enhance the moments that deepen customer personalization throughout the customer experience. 

How to create a customer journey map

There are many approaches to customer journey mapping, and ultimately there is no right or wrong answer.  However, having led journey mapping exercises at a few different brands, I can share what I have found to be most effective, for both process and output.

Step 1: Choose a customer persona

To begin, choose the customer persona that represents your typical or ideal customer.  Depending on how expansive you are with your personas, you may be inclined to run this exercise with two or three of them, but I’d suggest starting with your primary target, to provide focus. The objective is to keep that customer top of mind to experience your brand from their perspective.  

Whenever possible, this persona should be based on real data and insights about your target audience. They should include demographic information, behavioral patterns, motivations, and goals. (Your CDP is indispensable here, as it collects and unifies data from various sources, enabling the creation of precise and dynamic personas that reflect actual customer behaviors and preferences.)

Step 2: Identify key life stages

Next, identify the key life stages your customer advances through. These life stages are significant phases in the customer's interaction with your brand, such as:

• Prospect
• First-time purchaser
• Repeat purchaser
• Brand advocate
• Lapsed user  

If your brand has a subscription component (as is often the case these days with direct-to-consumer brands), you’ll want to break out “subscription purchase” from “one-time purchase” (sometimes called “a la cart”) and also include “cancel subscription.”  

Lastly, you’ll want to identify any meaningful micro-journeys, such as “gift purchase” or “refer-a-friend”.  Understanding these stages helps in creating a detailed and accurate map.

Step 3: Build a catalog of all touchpoints

This is the heart of the exercise: you’ll want to walk through the user experience for each life stage and catalog (screengrab) each touchpoint along the way.  

Depending on the life stage, these can include paid ads, social media, website experience, app experience, emails, SMS, and even customer care and direct mail. Your CDP helps with this step by providing a centralized view of most interactions, making it easier to identify and document touchpoints.

This is also where you’ll want to represent the physical delivery experience, if that applies to your brand.  

Step 4: Create a visual map for each life stage

Now that you’ve got the touchpoints cataloged, it’s time to bring the customer journey to life with a visual mapping for each life stage. This comprehensive view is a crucial component of the journey mapping exercise since it illustrates both the evolution of the customer experience and its consistency (or, often, lack of consistency) between life stages and across channels.

There are many software tools available that can help create and manage customer journey maps. These tools can provide templates, data integration, and visualization capabilities, making the mapping process more efficient and accurate. Many of these tools integrate with CDPs, allowing for real-time data updates and insights.

However, for a more immersive experience, you can create a temporary “Customer Journey Room.” This is an actual physical room where the touchpoints are taped on the wall. (I like to use a conference room that I can temporarily block off for a week or two.)

This physical space allows team members to walk through the journey, engage with the touchpoints, and better understand the customer's perspective. It also serves as a collaborative environment for the next step.

Step 5: Brainstorm opportunities and prioritize initiatives

Invite cross-functional participants to review and brainstorm on the customer journey. What gaps do people see in the experience? What opportunities do people see to expand engagement or better connect with customers?  

These sessions benefit from multiple fresh perspectives, leading to more comprehensive insights and innovative solutions. Consider incorporating teams like growth marketing, lifecycle marketing, product management, customer care, creative, and product development into these meetings. Different departments will provide unique viewpoints that might not be considered otherwise. 

After identifying pain points and gaps during the review sessions, document and prioritize the opportunities. Determine which issues need immediate attention and which ones can be addressed later. 

This prioritized roadmap helps in creating a clear action plan for improving the customer journey and is critical for ensuring that the customer journey mapping will bear fruit in an improved customer experience. 

Your map to improved customer engagement

Customer journey mapping is at the heart of understanding and improving customers’ engagement with your brand. By taking the time to assess and refine the customer journey, you can ensure a thoughtful and evolving experience that resonates with users. 

Additionally, a well-executed journey map, empowered by a CDP, fosters cross-company alignment, driving interdepartmental collaboration and uncovering new opportunities for growth and improvement. 

Investing in a customer journey map not only benefits the customer but also strengthens the overall business strategy, leading to sustained success and competitive advantage.

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When it comes to customer data, your organization has likely significantly invested in centralizing it — likely by using a cloud data warehouse like Snowflake. 

While this has yielded clear benefits in areas like data analytics and business intelligence, other departments, like marketing, are still struggling to realize the full value of this investment.

Despite adopting best-of-breed tools, your MarTech team faces integration challenges and untapped potential in customer data utilization. Additionally, there’s still “dark data” outside of your warehouse, representing critical information about customer engagement and your marketing efforts.

This article introduces the concept of data composability as a solution to these challenges. We’ll explore how a composable data strategy differs from traditional ones, the key aspects of a successful composable data strategy, 

Traditional data strategies vs. composable data strategies

A traditional data strategy centers on centralizing and managing customer data. It establishes best practices around governance, architecture, management, and quality, focusing primarily on the process of getting data into a central location. 

However, it often places less emphasis on how your organization will unlock value from the data once it’s centralized.

In contrast, a composable data strategy is driven by what you will do with your data. The primary goal of creating and modeling data is to power downstream applications. 

While you likely have a clear understanding of your analytical and predictive use cases, the data needs for CDP use cases — such as customer retention in marketing, acquisition in advertising, customer relationship management, and customer support — might not be as well-defined.

Key pillars of a composable data strategy

There are four key pillars of a composable data strategy:

1. Data creation
2. Data centralization
3. Data modeling
4. Data applications

Let’s dive into each pillar.

Customer data creation

While not the central focus of a CDP like Simon Data, data creation is critical. Instead of treating data as mere “exhaust” from business operations, we need to align data creation with CDP requirements. 

The Simon team can clarify these requirements, offer out-of-the-box data creation capabilities, and recommend best-in-class partners, like Snowplow, to optimize this stage of the data strategy.

Data centralization

Centralizing your data is about more than just moving it into a cloud data warehouse like Snowflake. It’s about ensuring that all relevant data — whether customer behavior, transactional records, or marketing interactions — is aggregated in a way that supports your broader data strategy. 

This centralization enables better data governance, easier access, and a stronger foundation for downstream applications.

Data modeling

Data modeling involves two key components: 

1. Organization-wide domain model
2. Specific customer model for your CDP 

The challenge is creating a data model that acts as a compatible “adapter” between your complex business model and the focused CDP model. This enables your CDP to support high-value data applications without being locked into a rigid or incompatible framework.

Applications

The real power of a composable CDP emerges when you apply your data. These applications span retention, acquisition, relationship management, and support. 

This composable approach allows you to adapt your data model to fit specific needs while feeding generated insights back into your data creation and centralization processes.

Why a composable data strategy and composable CDP work

You typically create, centralize, and model data on your own. However, a composable CDP plays a crucial role in adapting your data model to meet the specific needs of high-value applications. 

It also ensures that valuable data from these applications is captured and fed back into your data creation process — data that would otherwise be missed without a CDP integrating your marketing, advertising, and support teams into your overall data strategy.

Data interoperability

Now, let’s discuss data interoperability — how data is exchanged, communicated, and used between systems — between traditional data workflows and composable ones.

Raw data alone cannot power meaningful use cases; it needs to be adapted for downstream applications. 

Data interopability in a traditional CDP vs. a composable CDP

In a traditional CDP, this modeling work is typically done only after exporting data into the CDP itself, making it specific to CDP use cases and effectively “locking in” that data, preventing it from being used beyond the CDP.

 Data that flows into the CDP includes:

• Raw events: Capture real-time actions and behaviors of users across various touch points
• OLTP data: Includes critical transactional and user data, such as purchases, account details, and user interactions
• Product catalogs: Information about the products or services your organization offers, including inventory levels, descriptions, and pricing
• Fulfillment data: Tracks the status of orders, deliveries, and any logistics-related information
• Scored predictive models: Pre-computed insights and predictions, such as customer lifetime value or churn risk, help with segmentation and personalization

Data flowing back to the CDW includes:

• Segment membership: Information on which users belong to specific segments based on behaviors, attributes, or predictive scores
• Personalization: Data related to personalized content, product recommendations, and messaging tailored to individual users
• Campaign metadata and execution: Details about marketing campaigns, including objectives, targeting criteria, and execution data, such as send times and delivery status
• Downstream channel engagement: Data capturing how users interact with marketing efforts across channels, including email opens, ad clicks, and website interactions

But in a composable CDP, all data is in the CDW. This includes:

• Unified data hub: All data, whether it’s incoming raw events or outgoing segment memberships, remains centralized within the cloud data warehouse
• Flexible utilization: Because all data is housed in the CDW, it’s not locked into specific applications or use cases. It’s adaptable and accessible for a wide range of applications — from CDP use cases to advanced analytics, artificial intelligence, machine learning, and beyond
• Seamless integration: With Simon’s composable architecture, the modeling and enrichment work happens in the CDW, enabling the same datasets to power various applications without duplicating effort or data

The differentiated value of Simon’s composable architecture lies in its flexibility: you can perform all of this modeling work within the CDW. The benefit? You can leverage your data not only for CDP use cases but also for broader organizational needs, breaking free from the limitations of traditional CDPs.

The most important aspect of a composable data strategy: data applications

Let’s address the most critical aspect of a composable data strategy: the data applications you need to unlock with the data you’ve centralized within the cloud data warehouse.

As I said earlier, some data applications are more immediately accessible, given the state of the tooling ecosystem around the CDW and your organization's traditional team structure and skills composition.

For instance, your data science team is likely deeply embedded in your Snowflake instance.  They’re not only directly accessing your data through SQL, but their traditional workbench of Jupyter notebooks controlling and evaluating predictive models, seamlessly slots into the CDW via capabilities like Python for Snowpark and Snowflake’s baked-in support of tools like notebooks and DataFrames.

 You’ve likely already given them deep access to Snowflake, a computing budget, and perhaps even governance training and specific tooling workshops to further enable their interaction with your data.

Beyond BI and predictive modeling, there are additional high-value applications that are crucial to a composable data strategy. These applications often fall under the purview of marketing, advertising, and customer engagement teams.

• Segmentation: Create precise customer segments based on centralized data, enabling targeted marketing efforts that resonate with specific audiences. Segmentation within a composable CDP ensures consistency across all platforms and channels.
• Personalization: Deliver personalized experiences at scale by utilizing customer data stored in your CDW.  Personalization is driven by accurate, up-to-date profiles that reflect the latest customer interactions, ensuring relevance and engagement.
• Activation: Activate customer segments by syncing them with various marketing channels, ensuring that campaigns are data-driven and optimized for performance. Activation leverages the CDW's real-time capabilities, ensuring that data is fresh and actionable.
• Orchestration: Coordinate complex, multi-channel customer journeys that are responsive to real-time data.  Orchestration within a composable CDP ensures that all customer interactions are harmonized, creating a seamless experience across touch points.

The future of customer data management is composable

As organizations look to maximize the value of their data investments across departments like data, marketing, and sales, data teams need to adopt a composable data strategy. 

By leveraging a cloud data warehouse like Snowflake and a composable CDP like Simon Data, data teams can unlock the full potential of its customer data.

A composable data approach allows you  to:

1. Unify your data in a central hub
2. Adapt your data model flexibly to various use cases
3. Power high-value applications across your organization
4. Ensure consistent data utilization across all teams and tools

The result? A more agile, efficient, and effective data ecosystem that drives real business outcomes. But remember that a composable data strategy is not just about technology. It's about aligning it with your business goals, breaking down silos, and fostering a data-driven culture across your organization to drive the ultimate customer experience.

Have you ever bought more to save more? Many companies are built on this principle. You walk out of the store, spending way more than you intended because the sales were too good to miss!? That’s why even if bundling is not one of your primary business propositions (like it is for Costco), it should be in your repertoire.

BOGO is such a common bundle deal that it has its own acronym. Many customers have taken advantage of bundle pricing to save money, much to their benefit and your monetary gain. Using psychological principles and customer data, bundling can be turned into a discount strategy that wins sales. 

Bundling transforms product perception. By using zero and first-party customer data, you can tailor bundles to meet specific preferences and personalize them so there’s something enticing for each persona that shops with you. Let’s tap into the psychology that makes this tick.

The psychology behind product bundling

Bundling relies on simple psychological principles to draw us in. Any good marketing technique uses psychology as its backbone, and this is no different. A good bundle will attract customers with these factors:

• Enhanced value perception
• Simpler decision-making
• A completeness factor
• FOMO

Value perception

A two-pack of multivitamins for 20% less than two at full price is a good deal, right? You’ll buy another bottle next month anyway, so you might as well stock up. 

Customers see bundled products as having better value than individual items. This value-add drives successful bundling strategies. By presenting a bundle at a discounted price compared to the sum of individual prices, you make customers feel they are getting more for less, which boosts repeat sales and customer loyalty.

We mentioned this earlier, but Costco is the king of perceived value. The average Costco shopper spends $100 twice a month at the warehouse on megapacks of toilet paper, BOGO deals on pastries, and bulk grocery buys.

This marketing machine works because Costco knows its customers: affluent shoppers who have the excess money to buy in bulk. The company obsesses over research, customer data, and putting customers at the forefront of strategy.

Costco has plenty in its marketing toolbelt (like membership tiers and the $1.50 hot dog), but bundling definitely makes shoppers feel like the store adds value to their grocery run.

Simple decision-making

No one is surprised to learn that convenience is a key factor in most purchasing decisions. Bundling simplifies decision-making for customers. 

When faced with too many choices, customers can experience decision fatigue. This can lead shoppers to look elsewhere for a product, or, if everything feels too inconvenient, they may abandon their purchase altogether. 

Product bundles reduce the number of decisions a customer needs to make. This is essential if you’re selling something with a barrier to entry, like a product that requires specialist knowledge.

For instance, a starter bundle or essentials kit simplifies consumer decisions.  Someone just starting a hobby is probably wondering where to start and what they need. 

An amateur photographer, for example, is likely wondering which camera accessories are essential, which camera to buy, and even how invested they want to be in the hobby in the first place. A starter kit for a budding photographer might include:

• A camera body
• Memory card
• Basic lenses
• Carrying case
• Tripod
• Extra batteries

Voila! You’ve solved multiple problems with one bundle — and, if you’re savvy, you’ve priced it at a discount. The shopper doesn’t need to hit another store for a tripod, read reviews on other lenses, or spend even more time shopping around with competitors. 

Completeness factor

The completeness factor taps into the human desire for completeness. Why buy one piece of a collector’s set when you can purchase the whole thing?

Customers are more likely to buy a bundle that meets all their needs in one purchase rather than piecing together separate items. An example of this is in the skincare industry. 

Skincare brands espouse that products work best together when bought from a single brand with specific product routines. Rather than researching and buying products for individual concerns, you can purchase a bundle tailored to your skin type or based on your skin regime.

Urgency and FOMO

Creating a sense of urgency drives customers to act fast. Lim ited-time bundles capitalize on this psychological trigger, though the sense of urgency could be an illusion. Many companies use this technique around the holidays (BFCM, anyone?) for seasonal packages only available for special occasions.

Most limited-time bundles come packaged with a timer countdown. This builds FOMO because customers can see their opportunity to purchase the discounted deal wasting away.

Using customer data to bundle effectively

There are obviously many psychological triggers that draw customers to bundles. As marketers, we can capture customer data that helps us use the right psychological tricks to speak to our customers, which keeps bundling from feeling disingenuous.

Using customer data will set you apart from mediocre brands that simply copy-paste competitor tactics. Your product’s data isn’t replicable, so it can teach you exactly what you need to create tempting bundles as a customer discount strategy.

Purchase history

Take a look at customer purchasing trends; the proof is in the receipts.

Analyzing purchase history will help you identify which products customers buy together. Entire  algorithms rise and fall on this feature that capitalizes on FOMO and more straightforward decision-making:

What’s the peanut butter and jelly of your products? If you’re a haircare brand, that could be shampoo and conditioner. If customers buy those together, selling them as a bundle makes sense to save your shoppers time.

Take a look through customer purchases and scour for these trends. Or, have an algorithm in a Customer Data Platform do it for you!

Segmentation strategies

Segment your audience using customer data to personalize bundles better. You could segment the audience by:

• Repeat customers
• Buyers for specific product types
• Shoppers who’d benefit from retargeting
• Demographic data that influences purchase trends

Gathering and making sense of this data is time-consuming, so consider using a tool like a CDP to help you segment audiences in real-time. This will help you create bundles tailored to the right groups.

Customer feedback

Customer feedback is invaluable. Use customer signals to refine bundle strategies. These can be surveys, reviews, or a thumbs-up or thumbs-down feature asking if an algorithmic suggestion was helpful.

Regularly collect feedback to ensure your offers are attractive. Customers keep our assumptions in check with their feedback.

Types of product bundles

Ready to start packaging services together? A lot of work goes on behind the scenes to make a bundle work. These different types will reach the right audiences.

Pure bundles

Pure bundles comprise products that are only available as a bundle, not sold individually. Many service-based businesses will do this to avoid offering a service a la carte that consumes more energy than it’s worth. For instance, cable and phone companies typically offer pure bundles.

Pure bundles work well when combined products create a unique value proposition that separate purchases couldn’t replicate. But if you expect shoppers to want a single product or service, this shouldn’t be your sole strategy.

Mixed bundles

If pure bundles can’t be sold separately, mixed bundles can. Mixed bundles offer products that can be bought either individually or as part of a bundle. 

Customers prefer flexibility and personalization. Allowing them to mix and match between bundled and unbundled products appeals to a broader audience. Use mixed bundles if you have an audience that favors convenience (by bundling) and personalization (by buying separate items).

Brands usually keep the bundles attractive by pricing them at a discount, meaning customers who want individual items will go out of their way to pay more for those specific items.

Cross-sell bundles

Cross-sell bundles combine complementary products from different categories. Harkening back to the camera example, a cross-sell bundle pairs a camera with a memory card and a camera case. 

This strategy will encourage your audience to buy new items, and it’s an excellent way to introduce them to other product types and associate them with new services.

Tiered bundles

Tiered bundles are the bread and butter of service businesses. These types of bundles offer different levels of product or service that ascend in price, such as a basic, premium, and deluxe skincare set. 

This approach caters to different budget levels. The best part of this bundle type is that you can set the bundles side by side and compare their features, encouraging customers to upgrade.

Conclusion

Bundling creates irresistible product combinations. However, you need customer data to back your bundles up. Find the right combos that have the most appeal by looking at customer data such as:

• Wishlists (your customers are literally telling you what they want to buy)
• Purchases
• Audience segments
• Predictive analytics tools

Otherwise, you’ll reduce sales rather than boost your ROI. 

Data cloud platforms like Snowflake, combined with a CDP like Simon and other content delivery platforms are tools that help you store real-time customer data and act on it quickly. Implementing the best bundling pricing strategies is easiest and most accurate when the data analysis legwork is done using these tools.

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How do you turn customer data into those powerful lightbulb insights that drive marketing decisions? Even with powerful tools for aggregating, consolidating, and visualizing data, it’s not easy. 

Despite all the help we get, there’s room for human interpretation to create insights and roll them into our strategy. And where there’s room for human interpretation, there’s room for error.

Data is raw, unprocessed facts. Insights are the meaningful conclusions we draw from that data. Therein lies the challenge: we need quality, real-time data cleaned, normalized, and accurate. 

These days, data collectors grapple with privacy concerns, cookie deprecation, and poor quality data from disparate sources. To future-proof our targeting strategies and ensure they’re still personalized, we’ll need to access and activate zero- and first-party data effectively. We’ll also need to store our data in a way that mitigates human error or inconsistency from multiple sources.

Using the right tools for data storage and activation will get you to the insights stage faster. That’s why we have this framework for using Snowflake and a CDP (in this case, Simon Data) to get you to the strategy stage quickly.

The marketing data and insights disconnect

We’ve highlighted the challenge: Data and insights are not synonymous. Between the two is often a disconnect that’s the fault of changing regulations, data siloes, and manual workflows.

The shift from 3PC

Now that privacy regulations tighten on third-party data, marketers will need to be more economical with the information they have access to. The GDPR and CCPA regulations, in addition to the deprecation of third-party cookies (and cookies becoming opt-in only on Chrome), are really a good thing: they protect consumer privacy, which builds trust. 

In the future, marketers will depend more on zero- and first-party data. CDPs like Simon enable the seamless organization, access, and activation of zero- and first-party data, ensuring compliance with privacy regulations while still providing valuable customer insights.

Data silos and manual workflows between marketing teams

When every tool is clamoring to be insightful, it creates a lot of noise. One of your tools might provide accurate data on email deliverability, another on open rate, and another tool could track replies. How do you reconcile this data?

This is why many organizations struggle with disjointed data silos. Finding the most accurate picture of your email marketing strategy requires consolidating all this data from multiple sources into one place.

A manual workflow to consolidate data hinders your ability to quickly extract insights. Without quick insights, you can’t accurately segment your audience, personalize marketing, or change course with informed, data-driven decisions. Instead, your marketing strategy will be reactive.

Building a foundation for strong marketing data insights

So, how do we turn this boat around? Let’s begin with the end in mind, then work our way from there to the right data collection strategies.

Define your marketing goals

Good data insights start with clearly defined goals. Your goals drive the data insights you unearth. 

It’s like the scientific method: you start with a hypothesis so you can control variables and run a repeatable test. If your goal is a higher MQL rate, your hypothesis might be that on-page conversion efforts are falling flat. This gives you something to look for.

Unlike a scientific test, however, your data audits might run on multiple hypotheses at once. You might look at on-page conversion efforts, but you might also have a hypothesis that your ICP is unlikely to MQL.

Now that you have marketing goals (and therefore goals for your data analysis), how do you get to the bottom of these questions? Find the data sources and platforms relevant for your goals.

Quantitative data sources are typically straightforward — both to analyze and aggregate. Most  quality responses can be compiled in spreadsheets, in your martech tools, or with your CDP.

If you’re looking for qualitative insights, seek platforms to gather survey data, reviews, social media interactions, email correspondence, and so on. This is trickier to do, and it’s where manual workflows break down.

For this reason, it’s here that a data cloud platform and CDP will come in handy. These tools can save hours of manual work and reduce human error with a single source of truth. Snowflake would help you scalably store and process a large dataset, while a CDP like Simon helps unify and activate data.

Translate insights into the ultimate marketing strategy

Now, you have the data wrangled and readable. The insights you extract will depend on the goals you’ve established. At this step, you translate your findings into actionable marketing strategies.

Ideally, your insights can closely tie to KPIs or revenue — that’s the dream of every marketer. Tracking metrics like purchase history, customer segment behavior, campaign effectiveness, repeat purchases, and customer loyalty and retention metrics are all indicators that tie into revenue-creating activities. 

If we follow the scientific method, these insights lead to implications for further study — ergo, your strategies. Marketing is all a process of iterable testing, and your next strategies will be tests informed by previous experiments.

For example, if the data reveals a high rate of repeat purchases from a particular customer segment, you should develop personalized campaigns to further engage this group. Similarly, insights into campaign effectiveness can guide the optimization of future marketing efforts once you collect data on your success.

But this is where the directions get murky, isn’t it? That’s because there’s no one-size-fits-all step for this part. A marketing team has infinite ways to use data to inform strategy. Let’s get into an example of insight-to-strategy that helps this section feel more concrete.

Example: Personalized email campaigns

You manage email campaigns for a popular fashion retailer. Lately, you've been facing a perfect storm of declining engagement metrics: clicks, CTRs, open rates, and even subscriber count are plummeting. This isn't good news for your boss, and it's certainly not ideal for your career. You need a data-driven explanation and a solid plan to turn things around.

Luckily, you have a wealth of real-time customer data stored in Snowflake. By feeding this data into a CDP like Simon, you can unlock valuable insights.

With Simon's segmentation and audience analysis capabilities, you uncover distinct shopping behaviors among different customer groups. For instance, you've been bombarding everyone with emails about new arrivals, restock alerts, sales, and promotions. While this shotgun approach might have worked initially, it's now overwhelming customers and drowning out your message.

Your analysis reveals that new customers are more likely to unsubscribe after receiving new arrival emails, while loyal shoppers are ignoring beginner styling tips. It's clear that a one-size-fits-all email strategy is no longer effective. The solution? Segment your audience and tailor your email campaigns to each specific group.

In a perfect world, you dive into segmenting your audience with the help of your trusty CDP. Armed with customer data, you create highly specific segments. For instance, you identify a group of frequent shoppers who primarily purchase accessories. This segment becomes your "Accessory Addict" group. Another segment, "Weekend Warriors," consists of customers who make large purchases on Fridays and Saturdays.

With these segments defined, you craft tailored email campaigns. The "Accessory Addict" group receives emails highlighting new accessory collections, limited-edition pieces, and styling tips. The "Weekend Warriors" get emails with exclusive weekend deals, outfit inspiration, and personalized product recommendations based on their purchase history.

This level of personalization is a game-changer. Open rates soar, click-through rates skyrocket, and customer satisfaction improves. Your boss is thrilled with the results, and you've successfully transformed your email marketing strategy. 

While it's unlikely that you'll never need to experiment again, this data-driven approach has laid a strong foundation for future success — and at least your data is clean and actionable.

Building a data-driven culture

Now that your first adventure in data insights is over, the next step is baking data-backed decisions into company culture. Unsurprisingly, the key to breaking down silos and freely sharing data lies in communication between teams.

The best marketing insights come from frequent collaboration between data teams and marketers. Consider regular syncs and KPIs that depend on collaboration, encouraging you to work together. You can use your meetings to plan joint projects and sync on reporting.

Collaboration will also encourage you to break down data silos that impede the flow of information. Because you’ve likely integrated data from all your platforms into a single source of truth by this point in order to unearth the best insights, why stop? Make this standard procedure. If you have to commit to  a manual workflow to consolidate data, do this process regularly.

Lastly, keep learning and iterating. That’s how you avoid being stuck with tech debt and decades-old data software. Be sure to learn from other marketers regarding their hypotheses, the data models they use, and the insights they unearth.

Conclusion

With that, you have the lather-rinse-repeat of turning customer data insights into marketing strategies. It isn’t easy, and that’s why many marketers won’t do it. Fortunately, modern software makes it simpler than ever. 

If you haven’t given a CDP like Simon a chance, now’s a good time to start. Simon makes activating data nearly subconscious, requiring the ever-busy marketing team more time to focus on other aspects of the job. Learn more about how Snowflake and Simon work together to help marketers deliver the experience customers crave.

Think about all the different ways that a customer might interact with your brand or website.

They might like a tweet, Facebook status, or Instagram post. They might click key hyperlinks directly on your website. They might click on a search ad or display ad. They might put an item in their shopping cart, fill out a survey, sign up for a newsletter, or submit a customer review. And—hopefully!—they’ll buy something.

Now, think about where all of that data lives. For many businesses, it lives in multiple different systems—potentially dozens or more, depending on the size and complexity of your organization and the number of tools in your tech stack. 

With all of that data scattered about in disparate systems, it can be difficult for marketers to have a clear sense of who each customer is. And that means they aren’t being as effective as they could be in generating audiences, segments, marketing campaigns, and other assets. 

The solution? Centralize all of your customer data in one place by deploying a CDP: A customer data platform.

If you’re new to the world of CDPs, this article is for you. Below, we briefly define what a CDP is and walk through the six main types that you should know about. We also speak to the benefits of using a CDP and provide an example to help you get a better sense of the role such a platform can play in your business.

What is a CDP?

A customer data platform (CDP) is a type of data platform designed to import and integrate customer data from different sources. They organize your customer data into holistic customer profiles that update automatically, combining real-time and historical data into a single customer view as your customers engage with your brand—empowering your marketing team with more effective assets.

What are some of the main features of CDPs?

While there are many types of CDPs, most of them offer some or all of the following key functionalities:

• Data ingestion. CDPs import customer data from a variety of sources, centralizing it in a single location.
• Data unification. CDPs clean, transform, and gather data from all your different channels into one place so that it conforms to the schema you’ve defined for your customer data.
• Out-of-the-box integration. In addition to ingesting data from a variety of sources, most CDPs can also format and send data to other tools and databases, enabling easy automation of many data workflows.
• Identity resolution. Many CDPs offer identity resolution as an added feature (often through a partner company). Individual customers are identified so they can be tracked across the different channels where they might interact with your brand.
• Segmentation. Better CDPs offer customer segmentation. This real-time process allows businesses to easily define and target specific user segments, and can be entirely automated.

This highly effective functionality has made them quite popular. According to the market research firm Treasure Data, the CDP market was $5.1 billion in 2023 and is expected to hit $7.4 billion in 2024, hitting $28 billion by 2028. Over the next five years, the firm projects it will increase at a compound annual growth rate of nearly 40% per year.

With this growth has come a variety of platforms to choose from. The question is no longer, “Should I use a CDP?” but, “What kind of CDP should I use?” 

Different platforms will have different benefits depending on the size and goals of your business. In this blog we will look at the different types of CDPs and when each one is best.

Types of customer data platforms

Customer data platforms can be organized in a number of different ways, but most will fall into one of the following six categories:

1. Data streaming CDPs
2. Automation CDPs
3. Orchestration CDPs
4. Packaged CDPs
5. Composable CDPs
6. Marketing cloud (not a true CDP)

1. Data streaming CDPs

Data streaming CDPs sit on top of your existing databases, ingesting and centralizing customer data and (where possible) resolving customer identities to link all of the records of a customer’s interaction with the company across its various channels and platforms. 

Data streaming CDPs are great for things like tag management and streaming data collection, but they don’t always have some of the more marketing-oriented campaign automation features available with other types of CDPs. They can also be technically complex to implement and maintain.

2. Automation CDPs

Automation CDPs focus on making the execution of marketing campaigns easier and, as the name implies, more hands-off. Most of them were built with messaging in mind, so they’re particularly good at automating data assembly, segmentation, and the delivery of marketing messages via various channels such as email. 

Because they tend to have been built with that kind of workflow in mind, however, they’re often not great at integrating real-time data and adjusting messaging on the fly. Sometimes, this category is further broken down into automated analytics CPDs which focus on data analytics, and automated actions CDPs which focus on automating marketing processes.

3. Orchestration CDPs

Orchestration CDPs, also called smart hub CDPs, tend to have more features aimed at facilitating modern marketing workflows. They may even be integrated in tandem with data streaming CDPs, which feed them real-time data that are immediately integrated into campaigns. 

The right orchestration CDP, however, offers the best of both worlds: the data ingestion and centralization features of a data streaming CDP together with many of the marketing convenience features found in automation CDPs.

4. Packaged CDPs

A packaged CDP is a prebuilt customer data platform that is designed to be ready “out of the box” with all of the functions and features that a business could possibly want from its CDP. They tend to be very easy to implement, but come with significant drawbacks. 

First, in order to work and resolve customer identities, a packaged CDP needs to store a copy of the data that it collects—even if that data already lives in a data warehouse like Snowflake. This data redundancy means there is no single source of truth for an organization, and requires constant syncs between the CDP and the data warehouse. 

Second, because most packaged CDPs are intended to be an “all-in-one” solution, they will often contain many unused features and functionality, which can bloat the software and make it slower and more difficult to use.

5. Composable CDPs

A composable CDP can in many ways be thought of as the opposite of a packaged CDP. Whereas a packaged CDP is pre-built and standardized, a composable CDP is typically “unbundled”—i.e., it consists of individual modules that a business can either choose to use or do without. And whereas a packaged CDP needs its own copy of all of your customer data in addition to your data warehouse, a composed CDP sits directly on top of your existing data infrastructure—eliminating the need for duplication and all of the challenges it brings. 

6. Marketing cloud

Though not technically CDPs, marketing clouds are worth mentioning. These are multi-channel marketing solutions that integrate customer data from various channels. While they serve a purpose, many began as something else (often email marketing service providers, or ESPs) — as a result, they aren’t always capable of handling the complexities of modern customer data.

Benefits of customer data platforms

Customer data platforms bring all of your customer data under one roof, and in doing so offer a number of benefits for your business’s sales and marketing efforts. These include:

CDPs help you understand your customers better. 

CDPs make it easier to get a full understanding of who a customer is because they integrate all customer data—including demographic and personal data, engagement data, behavioral data, and even qualitative data.

For example, an ecommerce company will have data about social media engagement, ad views, email clicks, on-site content consumption, cart abandonment, purchases, returns, customer surveys, and much more. But each type of data often comes from a separate tool or service and thus lives in a separate place. CDPs automate the process of importing and unifying all of that data, making it much easier to see the big picture of a customer’s journey.

CDPs also make it easier to create effective segments by targeting customers using data points from a variety of sources that would be challenging to integrate without a CDP.

CDPs empower you to orchestrate communications across all channels. 

Because CDPs identify and group data from individual customers across all your channels, they are ideal for facilitating consistent cross-channel experiences.

When you identify a single user across multiple channels, you can offer a consistent, individualized experience no matter where they are. This personalized customer experience leads to better brand engagement and, ultimately, more conversions.

CDPs optimize team effectiveness and remove silos. 

Collecting lots of customer data in one place makes CDPs worth the price of entry for many companies. Without a CDP, teams must rely on each other to get the information they need. Marketing teams have to wait for data or engineering teams to build segments, which not only slows down the process but can also lead to errors in data interpretation. Putting the data straight into the hands of marketers allows for more accurate segmentation and lets the engineering and data teams focus on their own priorities.

CDPs empower you to market effectively in real time. 

CDPs are built to simultaneously ingest real-time and historical data, giving you the most accurate view of who your customer is at any given moment. This enables you to build even more personalized experiences.

For example, a CDP could enable a recommendation engine to suggest a product to a customer based on their earlier behavior in the same session. Without a CDP, that behavioral data would need to be manually transformed and likely moved to another database before it could be used by the recommendation engine.

CDP example: The Simon CDP

To better understand how all these features deliver on the benefits promised by CDPs, let’s take a look at a specific CDP — the Simon CDP, which can be considered both an orchestration CDP and a composable CDP.

Simon CDP uses batch and streaming processing to ingest different data from all kinds of sources. Both real-time and historical data are unified to create a single customer profile. As soon as specified events occur, the customer is identified and their profile is updated with valuable data.

From there, the Simon platform offers a variety of powerful features for enabling marketing workflows. For example, Simon CDP comes with intuitive tools like a no-code editor that allows anyone to build customer segments quickly. These segments are then used for everything from targeting digital marketing campaigns and email flows to analytics and reporting.

While its holistic customer view enables greater personalization, Simon CDP also offers an option for integrated machine learning (ML) models that draw on its unified customer data store to make product recommendations or predict customers at risk of churning. It also comes with intuitive features for A/B testing so you can see which messaging works best for a specific customer segment.

How to choose a customer data platform

Choosing the right CDP for your business will depend on your business use cases, your existing tech stack, and your goals. However, any request for proposal (RFP) for a CDP should include the following carefully evaluated potential solutions:

• Data management. Can the proposed solution ingest data from all your databases and channels? Can it process streaming data?
• Analytics and intelligence. Does the proposed solution provide intuitive analytics and reporting features that will enable your team to better understand your customers?
• Cross-channel orchestration. Will the proposed solution enable you to provide a more unified customer experience across all your channels via data syndication?
• Privacy, security, and compliance. Does the proposed solution allow you to easily manage and remove customer data in accordance with government regulations and best practices for security and privacy?
• Platform and services. What features does the proposed solution offer in comparison with others? What level of service can you expect from the vendor?

Want to see what Simon CDP can do for you? Request a demo today!

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Marketers today face an incredibly complicated and tricky state of affairs. 

On the one hand, customers are better informed than ever before, and they don’t hesitate to share their opinions with people all over the world. On the other, these customers demand an increasingly personalized experience and relationship with your brand.

These two realities mean that campaigns and strategies that worked well in the past can quickly become irrelevant, outdated, and obsolete with little more than a click, tap, or swipe. 

How can marketers stay ahead of the curve and become the most valuable people (MVP) for their companies? Here at Simon Data, we believe that the answer lies in using a customer data platform (CDP) to ensure that you’re leveraging all of your valuable customer data as efficiently and effectively as possible. 

Below, we take a closer look at what customer data platforms are, what they should be capable of doing, and why legacy tools are falling short. We also discuss how CDPs fill this gap, before walking through several potential use cases for CDPs within your organization. Finally, we compare CDPs against a number of other tools that they are often discussed alongside so that you’ll have a better sense of how CDPs differ from them and how your entire stack can potentially play together.

What is a customer data platform?

A customer data platform (CDP) is a marketing platform that helps you understand who your customers are and what motivates them to buy your product or interact with your brand. It does this in two key ways:

1. First, a CDP unifies customer data from disconnected data sources (like your company’s website or app, CRM, POS system, accounting system, and more) to create a single profile of each customer
2. These customer profiles can then be used to create more targeted segments and audiences and more personalized marketing campaigns (email, social, etc.).

In other words, a customer data platform (CDP) makes the right customer data available where you need it to activate your customers at the right moment. Think of it as your customer nervous system — it combines four key capabilities: data collection, profile unification, segmentation, and campaign orchestration.

What should a Customer Data Platform do?

Today’s marketing world is all about relationships between companies and consumers. The more meaningful the connection, the easier it is to grow and sustain a loyal base of enthusiastic people. However, this leaves marketers with a difficult dilemma: How can they build trusted relationships at the scale needed to remain either in a growth phase or at the scale they need to be to remain competitive?

The answer is in your data — from first-party and zero-party data you collect yourself all the way to third-party data you source from partners. Customer data is powerful, and brands need it to thrive in this constantly changing digital era. It is your biggest and most unique competitive advantage.

From the initial lead interaction to the storage and use of the data, all the way through a quality customer relationship, data makes a difference. And a CDP is the tool that helps you put this data to work.

CDP use cases

A CDP can help your business significantly improve your processes and move several important needles toward growth. This includes:

1. Increasing operational efficiency

The bottleneck between marketing and their technical partners in IT, data science, and engineering is very real and time-consuming. Anything that cuts down on that strain is appreciated by everyone.

A quality customer data platform democratizes this kind of critical customer intelligence so marketers can focus on marketing without the wait. It also allows transparency between departments and creates a central location for everyone to access the data they need to move forward. And with the advent of technologies like artificial intelligence (AI) and machine learning (ML) this can happen with greater speed and ease than ever before. 

2. Increasing revenue generation

The more available and accessible customer data becomes across the marketing program, the better you will know your customers. As a direct result, the speed at which new campaigns can be tested and deployed allows teams to do more and be more granularly personal, which generates more revenue.

The more insight into customer preferences and behavior you have, the better your decision-making will achieve business-level objectives. Customer data platforms streamline and support revenue generation throughout these processes in ways that other martech solutions simply can’t.

3. Reducing media spend

On average, media spend generally accounts for one-third of all marketing costs. A CDP can dramatically cut this number down.

For example, with the right data ingestion, analysis, and incorporation in place, suppression lists can update automatically instead of manually. Simultaneously, you can also increase ROAS with more fine-tuned retargeting audiences.

A customer data platform’s built-in testing capabilities feed your segmentation and overall customer intelligence. This directly optimizes campaigns and enables you to assess ROI across your media landscape for much less.

4. Streamlining technology costs

A CDP enables you to see your data all in one place. This in turn, allows you to focus on getting the most out of every capability in your current stack and eliminate excess spend on solutions with redundant functionality.

By being tech agnostic, you have the flexibility to assemble your plug-and-play dream team or to optimize your workflows around the strengths of suboptimal tech solutions with which you’re momentarily stuck.

5. Optimizing workflows

When your team has a home base from which they can operate the entire stack, the manual work of marketing becomes a natural extension of thinking. Before, this seemed like a pipe dream. Now, it can be a reality. That’s the power of centralized, organized data. And it’s only the beginning. 

Why don't other martech solutions fill this need?

In a nutshell, legacy tools only ever filled in a part of the great customer data puzzle. Most, if not all, legacy marketing technologies were purpose-built with a set amount of functions in mind. Not all of these have aged well and none of them provided a cohesive solution.

This led to many organizations and companies cobbling together a system of multiple moving parts. No single vendor has solved every facet of the customer experience and many company combinations simply can’t fill the gap.

Enter customer data platforms.

How customer data platforms fill the gap

Each step in a quality customer data platform’s workflow guides the user toward driving value with a full view of the customer, critical insights around an opportunity sizing, and profoundly embedded support for experimentation.

Here’s how CDPs accomplish this.

Technology can only achieve this by aggregating all customer data across any data source, then providing a smooth, intuitive interface. This enables marketers to leverage the data to target and personalize their campaigns and next steps.

A customer data platform’s abilities allow marketers to orchestrate customer experiences in and across channels while also providing rich insights on customer behavior and campaign performance.

Customer Data Platforms + other marketing tools

Do you find yourself confused by the sheer number of initials, acronyms, and abbreviations used in the martech world? Have you tried to figure out how all of the s

Do you find yourself confused by the sheer number of initials, acronyms, and abbreviations used in the martech world? Have you tried to figure out how all of the solutions interface with each other and your needs, only to find frustration and disappointment?

These struggles can be particularly acute when companies and organizations research customer data platforms (CDPs) and their relationship with other martech solutions. Typically, this is because of the sheer size and scope of the systems in question.

Below is a look at some of the most common martech tools that get discussed alongside CDPs, including what they offer, how they differ from CDPs, and how each system can potentially interface with a CDP.

Cloud Data Warehouse (CDW)

What they do: Cloud data platforms like Snowflake are cloud-based databases designed to aggregate, process, organize, and store data from multiple sources so that it can be analyzed and understood more readily than if the data was kept in multiple siloed systems. 

How they work with a CDP: Once a CDW has aggregated and organized your customer data, a CDP will have a much easier time using that information to generate customer profiles, segments, audiences, and campaigns. 

Data Management Platform (DMP)

What they do: DMPs, like The Trade Desk and Nielsen DMP, are platforms that collect anonymized third-party data, which is then specifically used to generate and manage paid digital advertising and marketing platforms.

How they work with a CDP: A CDP leverages the capacities of the DMP to bring highly-curated PII to your advertising campaigns.

Customer Relationship Management (CRM)

What they do: CRM tools like HubSpot and Salesforce collect and track customer information that your sales and services teams can use to manage customer relationships more effectively. In addition to basic customer information (name, contact information) these tools also track customer interactions, including the products they purchase and the website pages they visit, amongst other data points.

How they work with a CDP: CDPs can push audiences to CRM tools for streamlined downstream management and ingest data from CRM tools to support audience segmentation and personalization.

Email service provider (ESP)

What they do: An ESP is a software that empowers organizations to collect customer email addresses and basic information, which can then be used to create and manage email lists, audiences, and email marketing campaigns. 

How they work with a CDP: Businesses can generate email lists and campaigns within the CDP and then upload them to their ESP for execution, or they can forgo an ESP altogether and opt to let a CDP manage their email needs.

Multi-Channel Marketing Hub (MMH)

What they do: MMHs are a type of software designed to orchestrate personalized communications for your customers across multiple channels. These systems specialize in managing and deploying marketing campaigns to end channels like email, social media, or SMS.

How they work with a CDP: Typically, clients use one or the other, due to the considerable overlap between these systems.

Digital Personalization Engine (DPE)

What they do: A DPE tracks and analyzes customers’ previous interactions and predicted intent to identify the best user experience for that individual. It then alters the online experience through visual presentation, recommendations, or triggered messaging.

How they work with a CDP: CDPs sync audiences to DPE tools for downstream management and personalization.

Master Data Management Platform (MDM)

What they do: An MDM platform consists of various tools and processes that consolidate and manage an organization’s data so that it can be more effectively utilized by that organization — including for marketing purposes. In this regard, they are similar to CDWs.

How they work with a CDP: When used together, MDMs and CDPs can provide an amazing overview of the entire company’s data as a whole.

Data Lake

What they do: A data lake is a centralized repository designed to store all of an organization’s data — in its original form. This can include structured, unstructured, and semi-structured data from any source.

How they work with a CDP: CDPs can share all customer data to a client’s data lake, streamlining the process of sharing information.

Simplify the CDP conversation within your organization

One of the challenges of acquiring a customer data platform (CDP) is that it can be unclear who should own the process. The muddle typically happens between Marketing, IT, and Production. However, you can easily avoid this confusion if you start with the proper processes in place.

Are you considering the role that a CDP might be able to play within your organization? Download our CDP Buyer’s Guide, which includes helpful tips you can use to evaluate your options and effectively guide the conversation.

New and seasoned marketers know that “personalization” is not just another trend. It’s become a key component of ecommerce marketing strategies so brands can maintain their competitive edge.

More than half (61%) of executives recently noted that personalizing the customer experience is a high priority for their brand.Executives aren’t claiming its importance for no reason, either – 76% of consumers indicate they are more inclined to purchase from brands that personalize, and these consumers spend 37% more with those brands.

Personalization is here to stay. Don’t just take our word for it, either. Studies have shown personalization can reduce customer acquisition costs by up to 50%, lift revenue by 5-15%, and increase marketing ROI by 10-30%.

While personalization offers quantifiable benefits, enterprise ecommerce businesses still struggle to effectively tailor customer journeys to drive more revenue.

This article explores these challenges and how Customer Data Platforms (CDPs) like Simon Data can help enterprise ecommerce businesses overcome these obstacles by unlocking the full potential of customer data for a personalized shopping experience.

The challenges of personalization for ecommerce companies

As personalization continues to prove its value, it also presents challenges for enterprise ecommerce businesses. These challenges can lead to missed opportunities for increased customer engagement and loyalty, ultimately affecting revenue.

In a 2024 report by Deloitte, only 43% of surveyed consumers acknowledged that their online shopping experience was personalized, contrasting with 61% of brands that reported personalizing their marketing campaigns.

Remember: more than 76% of consumers say they’re more likely to purchase from brands that personalize. So why does this disparity exist?

This disconnect between ecommerce businesses and their consumers often stems from:

Data silos: For ecommerce businesses, data often resides in various systems. For instance, customer data can be found scattered across CRM, marketing automation, and analytics tools. As a result, marketing teams struggle to analyze and fully understand customer behavior, preferences, and purchase history.

Fragmented customer profiles: Customer data can also be inconsistent and incomplete. This leads to inaccurate profiles that hinder personalization efforts. Businesses cannot deliver targeted content, relevant product recommendations, and timely promotions without accurate and holistic data.

Lack of actionable Insights: Even if customer data was not siloed and complete, without the ability to translate this information into meaningful actions, its value diminishes. This can result in generic and impersonal interactions that fail to resonate with the customer, impacting conversion rates and customer loyalty. 

Data governance & security: Enterprise businesses must navigate strict data governance and security requirements. 

Compliance with regulations such as the General Data Protection Regulation (GDPR) and California Consumer Privacy Act (CCPA) is essential to protecting customer data and maintaining trust.

These regulations impose guidelines on customer data collection, storage, processing, and sharing.

These unfortunate but common scenarios can impact any ecommerce brand’s ability to create cohesive, personalized customer experiences. 

CDPs address these challenges by ingesting data from scattered sources and marrying it into a customer 360. This allows ecommerce marketers to take action on their customer data and identify the data gaps needed for better personalization.

Additionally, most CDPs, including Simon Data, prioritize data governance and security requirements so enterprise marketing teams can rest easy knowing their customer data is protected.

How CDPs help enterprise ecommerce brands thrive

CDPs address common challenges and can contribute to the success of ecommerce brands. A CDP can improve conversion rates, drive increased average order value, and even foster better customer retention and loyalty. Simply put, when used effectively, a CDP can help your ecommerce brand flourish.

This is especially true for enterprise ecommerce businesses as CDPs help larger organizations with:

• Managing complex data ecosystems that include numerous data sources and multiple integrations.
• Achieving a customer 360 view by consolidating disparate customer data. This creates a single customer profile that provides detailed browsing behavior and purchase history.
• Enhancing segmentation capabilities so the marketing team can leverage rich customer data to create granular audiences based on demographics, behavior, and purchase history.
• Personalization at scale, such as custom product recommendations, targeted marketing campaigns, and dynamic content delivery across all engagement.

CDPs empower ecommerce marketing teams to orchestrate customer experiences as well. A tool like this helps ecommerce brands engage customers throughout the journey with precision and intent – creating a personalized online shopping experience across channels (website, email, mobile app, etc.). This ensures consistency and relevance across each customer touchpoint.

Let's break down the customer journey into three phases: acquisition, engagement, and retention.

First, ecommerce brands need to acquire new customers. This is done through brand awareness and engaging with potential customers. Some CDPs, like Simon, can integrate with social media or programmatic ad vendors, such as The Trade Desk, for brands to use first-party data. 

Creating a lookalike audience of your most valuable customers will potentially bring in additional high-value customers, for example. 

As customers start researching and evaluating the product/service, ecommerce brands want to engage with customers. 

Enhancing the customer's pre-purchase experience might involve targeted digital ads, product suggestions based on browsing history, or sending timely messages in response to customer behavior (like abandoned cart reminders) – all of which a CDP can do.

Personalization continues beyond the initial purchase. During the retention phase, ecommerce brands pivot towards fostering customer loyalty, which improves Customer Lifetime Value (CTLV). 

CDPs provide essential capabilities for brands to implement strategies aimed at increasing repeat purchases, upselling or cross-selling related products, establishing loyalty programs, and maintaining customer satisfaction.

A customer story: RCI

Resort Condominium International, a Simon Data customer, saw notable results with their new website personalization strategy. Personalized website content increased bookings from their “not likely to book” segments by 4.5% and generated $13MM in revenue.

While RCI only reported on web content personalization, imagine what the results could be from personalizing across all channels across all customer touchpoints. With the right set up and integrations, CDPs can optimally personalize a seamless omnichannel experience.

Conclusion

At Simon, I’ve been hearing from customers about how difficult it is for enterprise marketers to access, secure, and activate the gold mine of customer data they already have to meet consumers’ demands for personalization. 

But this is just the beginning. According to a 2022 study by IDC, the volume of data (created, captured, replicated, and consumed) in the world is expected to double by 2026. That’s an estimated 100,000 exabytes (EB) worth of data!

Consumers already want relevant and timely experiences. With the increasing amount of data, enterprise ecommerce brands must maximize its use to drive personalized experiences.

A CDP offers the essential functionalities for ecommerce marketing teams to fully leverage customer data by streamlining data management and providing robust orchestration capabilities. 

CDPs also enable enterprise ecommerce businesses to effectively utilize data-driven personalization through single customer views.

Don’t overlook the vast amount of customer data currently available and the unfathomable amounts to come. Learn how to take your ecommerce business to the next level by fully harnessing the potential of your customer data.

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